Use frozen BN only if pre-trained. (#5443)

torchvision/models/detection/faster_rcnn.py

@@ -383,15 +383,15 @@ def fasterrcnn_resnet50_fpn(
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
             passed (the default) this value is set to 3.
     """
-    trainable_backbone_layers = _validate_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
-    )
+    is_trained = pretrained or pretrained_backbone
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
     if pretrained:
         # no need to download the backbone if pretrained is set
         pretrained_backbone = False
 
-    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
     model = FasterRCNN(backbone, num_classes, **kwargs)
     if pretrained:
@@ -410,16 +410,14 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
     trainable_backbone_layers=None,
     **kwargs,
 ):
-    trainable_backbone_layers = _validate_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers, 6, 3
-    )
+    is_trained = pretrained or pretrained_backbone
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 6, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
     if pretrained:
         pretrained_backbone = False
 
-    backbone = mobilenet_v3_large(
-        pretrained=pretrained_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d
-    )
+    backbone = mobilenet_v3_large(pretrained=pretrained_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _mobilenet_extractor(backbone, True, trainable_backbone_layers)
 
     anchor_sizes = (

torchvision/models/detection/fcos.py

@@ -686,15 +686,15 @@ def fcos_resnet50_fpn(
             from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are
             trainable. If ``None`` is passed (the default) this value is set to 3. Default: None
     """
-    trainable_backbone_layers = _validate_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
-    )
+    is_trained = pretrained or pretrained_backbone
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
     if pretrained:
         # no need to download the backbone if pretrained is set
         pretrained_backbone = False
 
-    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _resnet_fpn_extractor(
         backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256)
     )

torchvision/models/detection/keypoint_rcnn.py

@@ -365,15 +365,15 @@ def keypointrcnn_resnet50_fpn(
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
             passed (the default) this value is set to 3.
     """
-    trainable_backbone_layers = _validate_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
-    )
+    is_trained = pretrained or pretrained_backbone
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
     if pretrained:
         # no need to download the backbone if pretrained is set
         pretrained_backbone = False
 
-    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
     model = KeypointRCNN(backbone, num_classes, num_keypoints=num_keypoints, **kwargs)
     if pretrained:

torchvision/models/detection/mask_rcnn.py

@@ -360,15 +360,15 @@ def maskrcnn_resnet50_fpn(
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
             passed (the default) this value is set to 3.
     """
-    trainable_backbone_layers = _validate_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
-    )
+    is_trained = pretrained or pretrained_backbone
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
     if pretrained:
         # no need to download the backbone if pretrained is set
         pretrained_backbone = False
 
-    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
     model = MaskRCNN(backbone, num_classes, **kwargs)
     if pretrained:

torchvision/models/detection/retinanet.py

@@ -626,15 +626,15 @@ def retinanet_resnet50_fpn(
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
             passed (the default) this value is set to 3.
     """
-    trainable_backbone_layers = _validate_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
-    )
+    is_trained = pretrained or pretrained_backbone
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
     if pretrained:
         # no need to download the backbone if pretrained is set
         pretrained_backbone = False
 
-    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(pretrained=pretrained_backbone, progress=progress, norm_layer=norm_layer)
     # skip P2 because it generates too many anchors (according to their paper)
     backbone = _resnet_fpn_extractor(
         backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256)

torchvision/prototype/models/detection/faster_rcnn.py

 from typing import Any, Optional, Union
 
+from torch import nn
 from torchvision.prototype.transforms import CocoEval
 from torchvision.transforms.functional import InterpolationMode
 
@@ -103,11 +104,11 @@ def fasterrcnn_resnet50_fpn(
     elif num_classes is None:
         num_classes = 91
 
-    trainable_backbone_layers = _validate_trainable_layers(
-        weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3
-    )
+    is_trained = weights is not None or weights_backbone is not None
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
-    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
     model = FasterRCNN(backbone, num_classes=num_classes, **kwargs)
 
@@ -134,11 +135,11 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
     elif num_classes is None:
         num_classes = 91
 
-    trainable_backbone_layers = _validate_trainable_layers(
-        weights is not None or weights_backbone is not None, trainable_backbone_layers, 6, 3
-    )
+    is_trained = weights is not None or weights_backbone is not None
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 6, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
-    backbone = mobilenet_v3_large(weights=weights_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = mobilenet_v3_large(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _mobilenet_extractor(backbone, True, trainable_backbone_layers)
     anchor_sizes = (
         (

torchvision/prototype/models/detection/fcos.py

 from typing import Any, Optional
 
+from torch import nn
 from torchvision.prototype.transforms import CocoEval
 from torchvision.transforms.functional import InterpolationMode
 
@@ -63,11 +64,11 @@ def fcos_resnet50_fpn(
     elif num_classes is None:
         num_classes = 91
 
-    trainable_backbone_layers = _validate_trainable_layers(
-        weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3
-    )
+    is_trained = weights is not None or weights_backbone is not None
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
-    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _resnet_fpn_extractor(
         backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256)
     )

torchvision/prototype/models/detection/keypoint_rcnn.py

 from typing import Any, Optional
 
+from torch import nn
 from torchvision.prototype.transforms import CocoEval
 from torchvision.transforms.functional import InterpolationMode
 
@@ -91,11 +92,11 @@ def keypointrcnn_resnet50_fpn(
         if num_keypoints is None:
             num_keypoints = 17
 
-    trainable_backbone_layers = _validate_trainable_layers(
-        weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3
-    )
+    is_trained = weights is not None or weights_backbone is not None
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
-    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
     model = KeypointRCNN(backbone, num_classes, num_keypoints=num_keypoints, **kwargs)
 

torchvision/prototype/models/detection/mask_rcnn.py

 from typing import Any, Optional
 
+from torch import nn
 from torchvision.prototype.transforms import CocoEval
 from torchvision.transforms.functional import InterpolationMode
 
@@ -64,11 +65,11 @@ def maskrcnn_resnet50_fpn(
     elif num_classes is None:
         num_classes = 91
 
-    trainable_backbone_layers = _validate_trainable_layers(
-        weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3
-    )
+    is_trained = weights is not None or weights_backbone is not None
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
-    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
     backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
     model = MaskRCNN(backbone, num_classes=num_classes, **kwargs)
 

torchvision/prototype/models/detection/retinanet.py

 from typing import Any, Optional
 
+from torch import nn
 from torchvision.prototype.transforms import CocoEval
 from torchvision.transforms.functional import InterpolationMode
 
@@ -64,11 +65,11 @@ def retinanet_resnet50_fpn(
     elif num_classes is None:
         num_classes = 91
 
-    trainable_backbone_layers = _validate_trainable_layers(
-        weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3
-    )
+    is_trained = weights is not None or weights_backbone is not None
+    trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
+    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
 
-    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+    backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
     # skip P2 because it generates too many anchors (according to their paper)
     backbone = _resnet_fpn_extractor(
         backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256)